Epidemiology Without Math

Disease doesn't just appear magically one day in an otherwise healthy person. It takes time to develop, often first manifesting in childhood. In this lecture, we look at how disease develops through the lifespan, and what this means for how we detect, measure, and treat it.

In this section, students will learn about the fundamental designs of medical research projects, their advantages and disadvantages, and what they can and can't tell us.

Bias is the intellectual underpinnings of Epidemiology. Why are some statistics or study findings more believable than others?

Reliability and validity go hand in hand. But what are they? And why are they so important to medical research design and interpretation?

In Epidemiology, the word "experiment" means something very specific. The most well known example is the RCT, or randomized controlled (or clinical) trial. You can't understand medical research if you don't understand RCTs.

Epidemiologists are often described as experts in causal inference. But if you can't run an RCT, how do you really know that A causes B? In this lecture, we look at Bradford Hill's criteria for establishing causal relationships.

Surveillance has a "Big Brother" sound to it. But it's a hallmark of public health. Surveillance systems allow us to keep an eye out for spikes in disease incidence, and to motivate our limited resources accordingly.

Systematic reviews are considered the pinnacle of medical evidence. But what are they? How are they conducted? What are their strengths and weaknesses?